Computational expression deconvolution in a complex mammalian organ

Abstract

Background: Microarray expression profiling has been widely used to identify differentially expressed genes in complex cellular systems. However, while such methods can be used to directly infer intracellular regulation within homogeneous cell populations, interpretation of in vivo gene expression data derived from complex organs composed of multiple cell types is more problematic. Specifically, observed changes in gene expression may be due either to changes in gene regulation within a given cell type or to changes in the relative abundance of expressing cell types. Consequently, bona fide changes in intrinsic gene regulation may be either mimicked or masked by changes in the relative proportion of different cell types. To date, few analytical approaches have addressed this problem.

Results: We have chosen to apply a computational method for deconvoluting gene expression profiles derived from intact tissues by using reference expression data for purified populations of the constituent cell types of the mammary gland. These data were used to estimate changes in the relative proportions of different cell types during murine mammary gland development and Ras-induced mammary tumorigenesis. These computational estimates of changing compartment sizes were then used to enrich lists of differentially expressed genes for transcripts that change as a function of intrinsic intracellular regulation rather than shifts in the relative abundance of expressing cell types. Using this approach, we have demonstrated that adjusting mammary gene expression profiles for changes in three principal compartments--epithelium, white adipose tissue, and brown adipose tissue--is sufficient both to reduce false-positive changes in gene expression due solely to changes in compartment sizes and to reduce false-negative changes by unmasking genuine alterations in gene expression that were otherwise obscured by changes in compartment sizes.

Conclusion: By adjusting gene expression values for changes in the sizes of cell type-specific compartments, this computational deconvolution method has the potential to increase both the sensitivity and specificity of differential gene expression experiments performed on complex tissues. Given the necessity for understanding complex biological processes such as development and carcinogenesis within the context of intact tissues, this approach offers substantial utility and should be broadly applicable to identifying gene expression changes in tissues composed of multiple cell types.

Figure 1

Expression profiles of BAT-specific genes and estimated changes in cell compartment sizes during mammary gland development. (a) Normalized expression profile of Cidea across mammary gland development stages showing marked deviation during late pregnancy and lactation from the average profile of 10 BAT-specific genes listed in Table 1. Mean expression (±SEM) is shown at each development stage, including nulliparity (Nullip), pregnancy (Preg), lactation (Lact), and involution (Inv). (b) Calculated proportions of brown adipose tissue (BAT), white adipose tissue (WAT), and mammary epithelial cell (MEC) compartments in pure reference samples and mammary tissues at the indicated mammary developmental time points (mean ± SEM).

Figure 2

Mammary gland morphology during the development. (a) Analysis of carmine-stained whole mounts (magnification 6×) and (b) hemotoxylin and eosin (H&E)-stained sections (magnification 100×) showing the relative changes in size of the epithelial and adipose compartments of the number 4 mammary gland at the indicated time points during mammary gland development.

Figure 3

Tubulin and cytokeratin 8 expression during mammary gland development. (a) Predicted (dashed lines) and observed (solid lines) normalized expression for α-tubulin and γ-tubulin during mammary gland development. A high correlation was found between observed expression profiles and predicted profiles calculated based on changes in cell compartment sizes. (b) Immunofluorescence staining of cytokeratin 8 (Krt2-8) showing downregulated expression in the mammary epithelium at d18 of pregnancy compared to 10 wk of nulliparous develoment.

Figure 4

Changes in cell compartment sizes following Ras activation in the mammary gland. (a) Calculated proportions of BAT, WAT, and MEC compartments in the mammary gland at the indicated days following oncogenic Ras activation in MTB/TRAS mice (mean ± SEM). Ras activation expands the epithelial compartment and decreases the adipocyte compartment. (b) Carmine-stained whole mounts (magnification 10×) and H&E-stained sections (magnification 100×) of number 3 mammary glands in MTB/TRAS mice showing changes in the sizes of cell compartments following oncogenic Ras activation similar to those predicted by deconvolution analysis.

Figure 5

Cytokeratin 8 expression and apoptosis in the mammary gland following Ras activation. (a) Immunofluorescence staining for cytokeratin 8 (Krt2-8) in the mammary gland at d0 and d4 following oncogenic Ras activation revealed no significant changes in its expression within epithelial cells. (b) TUNEL analysis showing that Ras activation does not increase apoptosis rates in the mammary glands following four days of Ras activation.

Figure 6

Estimated changes in the relative abundance of stromal cell types. Calculated relative abundance (mean ± SEM) of stromal cell types (a) during mammary gland development or (b) following oncogenic Ras activation. Expression levels shown are the average of normalized expression levels of the tissue-specific genes for each tissue/cell type listed in Table 1.

Figure 7

Upregulation of Snail by Ras activation. (a) Normalized microarray expression profiling (mean ± SEM) or (b) Northern hybridization analysis of Snail expression as a function of oncogenic Ras activation.